CN117894158B - Cold and tide disaster risk pre-assessment method based on intelligent grid air temperature prediction - Google Patents
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Abstract
The invention provides a method for pre-evaluating the risk of a cold tide disaster based on intelligent grid air temperature prediction, which is characterized in that based on intelligent grid air temperature prediction data, a pre-evaluation risk index of the cold tide process is calculated, based on the pre-evaluation risk model of the cold tide process is built by combining exposure degree and vulnerability information of a disaster-bearing body, and the risk pre-evaluation model is used for carrying out risk prediction on the disaster-bearing body such as high-resolution population, domestic total production (GDP) and wheat, and the like, and the pre-evaluation product can effectively compensate weather service limitation which only depends on weather element prediction, enrich weather service information such as disaster probability, hazard degree and the like of the cold tide disaster process in time intervals, industries and regions, and provide scientific decision support for effectively protecting the cold tide disaster, deploying disaster defenses in advance, reducing disaster loss and the like.
Description
Technical Field
The invention belongs to the technical field of meteorological science, and particularly relates to a cold tide disaster risk pre-evaluation method based on intelligent grid air temperature prediction.
Background
In the climate warming background, although the average air temperature is increased, the extremely low temperature event is frequent, and serious influence is brought to the aspects of society, economy, ecology and the like, wherein the occurrence frequency of the extremely low temperature event is obviously increased, and the influence is continuously enhanced. In winter and spring, disastrous weather such as low temperature, frost damage, chill and the like can occur in all places of the whole province, and the influence and the caused loss of the low-temperature weather disaster and the derivative disaster on agriculture are serious.
After 2000 years, domestic and foreign scholars gradually rise in exploring and researching the aspects of disaster risk and risk assessment caused by meteorological disasters, especially in extreme disaster weather frequent in recent years, and more scholars are put into researching the evolution characteristics and the great risks of high-influence extreme meteorological disaster events. Developing weather disaster risk early warning business and research based on influence becomes consensus, and is an important development direction of disaster prevention and reduction in the future. A plurality of students use long-sequence meteorological observation data, a method such as a hierarchical analysis method, gray correlation degree, an information entropy weight method and the like is utilized to select a meteorological disaster factor, a risk and risk assessment index system is established according to the occurrence frequency and the occurrence intensity of the meteorological disaster, and risk characteristic assessment of the meteorological disaster to specific industries such as agriculture, traffic and the like is carried out. However, most of these researches are to conduct risk division on the occurred weather disasters, and the weather background of the weather disasters is evaluated, and compared with the weather background, the pre-evaluation of disaster causing dangers and the risk early warning based on the influence aiming at a certain disaster weather process have more important significance in the weather disaster defense. Since the first national natural disaster risk census work was carried out in 2020, the meteorological department has made some staged progress in terms of meteorological disaster risk assessment, risk prediction, etc., however, the technical research in these aspects is still in the preliminary summary and exploration stage. In order to meet the increasingly improved weather service demands, the development of dangerous and risk pre-evaluation technologies for the disastrous weather process is urgently required to be quickened, and the pre-evaluation and risk early warning business process for the disastrous weather process based on influence is promoted.
Disclosure of Invention
The invention aims to: aiming at the condition that the prior art cannot better meet the agricultural meteorological service demand, the invention provides a method for pre-evaluating the risk of a cold tide disaster based on intelligent grid air temperature prediction.
The technical scheme is as follows: in order to achieve the above purpose, the present invention adopts the following technical scheme: a cold tide disaster risk pre-evaluation method based on intelligent grid air temperature prediction comprises the following steps:
Step S1, selecting and obtaining weather disaster factors of the cold tide disasters by using disaster damage curves according to cold tide disasters and disaster history data of national observation stations and regional observation stations, wherein the weather disaster factors of the cold tide disasters comprise the lowest temperature cooling range of 48 hours Cumulative temperature reduction margin of daily minimum air temperature/>Extreme minimum air temperature/>And duration of the period of cold；
Step S2, screening a plurality of country observation stations within a set distance range of a certain area observation station, and fitting the country observation stations with data sequences of the same time period of the area observation station respectively by a partial least square method to obtain a statistical relationship and a correlation coefficient of the country observation stations; the data sequence is a long-time data sequence of the lowest daily air temperature and the average daily air temperature;
using the country observation station with the largest corresponding correlation coefficient as a fitting object, and expanding and reconstructing the data sequence of the regional observation station to the same time range as the corresponding country observation station by using a partial least square method through the statistical relation of the country observation station;
The expansion reconstruction of the data sequences of the other regional observation stations is completed according to the same method;
step S3, constructing a disaster risk assessment model of the cold weather process based on intelligent grid air temperature forecast to obtain a risk pre-assessment index of the cold weather process of each site;
s4, comprehensively considering a pre-evaluation risk index of the cold weather process, and combining a disaster-bearing body to establish a cold weather process risk pre-evaluation model of the disaster-bearing body, wherein the disaster-bearing body comprises population, economy and wheat;
Step S5, based on the disaster risk assessment model of the cold weather process and the risk pre-assessment model of the cold weather process, the product is estimated by generating the disaster risk and risk of various cold weather processes in time intervals, industries and areas.
Further, the calculating of the risk pre-evaluation index in the cold weather process in step S3 includes the following steps:
(1) Normalizing the cold and damp disaster weather disaster factors of each observation station according to the following formula (1) to obtain normalized tidal disaster weather disaster factors ，
（1）
In the formula (1), the components are as follows,Data representing weather disaster factors of cold and damp disasters of the ith observation station,/>Representing the minimum value of weather disaster causing factor data of cold tide disasters of observation station,/>Representing the maximum value of the weather disaster causing factor data of the cold tide disasters of the observation station;
(2) Weight determination of the weather disaster-causing factors of the cold and the tide:
Firstly, calculating an index value of the jth cold weather process under the ith disaster causing factor according to the formula (2) Specific gravity of the index，
（2）
In the formula (2), m represents the total number of disaster causing factors; n represents the number of times of the weather process of the chill;
Then, the entropy value of the ith disaster causing factor is calculated by an information entropy weight method As shown in the formula (3),
（3）
Then, the objective weight of the ith disaster causing factor is obtained through the calculation of the formula (4)，
（4），
(3) Constructing a risk prediction evaluation model of the cold weather process through the method (5) to obtain a risk prediction index of the cold weather process of each site，
（5）
In the formula (5), the amino acid sequence of the compound,Represents the lowest temperature drop range of 48 hours/dayRepresenting the cumulative temperature drop amplitude of the lowest daily air temperature,/>Representing extreme minimum air temperature,/>The duration days of the cold tide process are represented, and A, B, C and D represent corresponding weight coefficients.
Further, in the step S4, a risk pre-evaluation model of the disaster-bearing body in the cold tide weather process is established through the step (6),
（6）
In the formula (6), the amino acid sequence of the compound,Representing risk prediction index of k-type disaster-bearing body,/>The risk prediction index of the weather process of the cold and damp of the k-th disaster-tolerant body is represented;
The exposure degree of the k-type disaster-bearing body is represented by the density of people and mouth in the area, the GDP of the ground and the proportion of the wheat planting area, the representation relational expression is shown as the following formula (7),
（7）
In the formula (7), S represents the total area of the area or the cultivated land area,The number of disaster-bearing bodies or the planting area in the area are represented,
The vulnerability index representing the k-type disaster-bearing body is obtained through the calculation of the formula (8),
（8）
In the formula (8), the amino acid sequence of the compound,Represents the area of direct economic loss or disaster in the population under 14 years old and over 65 years old, and S is the total population, the total domestic production value or the total crop planting area.
The beneficial effects are that: compared with the prior art, the method has the following advantages:
(1) By adopting the method, on the basis of providing the traditional air temperature element forecast, the risk forecast in the process of the disastrous weather of the chill and the risk forecast products aiming at the specific disaster bearing body are increased, and the effective transition from the traditional element forecast to the risk forecast and the forecast is realized.
(2) The disaster risk and risk prediction product generated by the method greatly enriches the form and service content of the decision-making weather service product, and provides scientific decision support for effective application of the government to cold and tide disasters, early deployment of disaster defense measures, reduction of disaster loss and the like.
Drawings
FIG. 1 is a logic flow diagram of a method for pre-evaluating risk of a cold tide disaster based on intelligent grid air temperature prediction according to the present invention;
Fig. 2 is a graph of the best linear fit of the present invention for the lowest daily air temperature data of 24 regional weather stations and surrounding country stations in the range 2010-2020 in the copper mountain area of the city of Xuzhou in Jiangsu province, wherein: (a) is Zheng Jizhen, (b) is Zhang Jizhen, (c) is Mao Cunzhen, and (d) is Liu Jizhen.
Detailed Description
The present application is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the application and not limiting of its scope, and various modifications of the application, which are equivalent to those skilled in the art upon reading the application, will fall within the scope of the application as defined in the appended claims.
The invention discloses a cold tide disaster risk pre-evaluation method based on intelligent grid air temperature prediction, which comprises the following steps of:
The first step: and selecting weather disaster factors of the cold and damp disasters by using a disaster damage curve analysis method according to the lowest daily air temperature data of the national stations and regional stations in Jiangsu and combining the information of the cold and damp disasters and the disaster conditions. According to DB 32/T1199-2008 Meteorological disaster definition and classification and GB/T21987-2017 Cold tide grade, after cold tide refers to cold air in a certain area passes through, the temperature is reduced by more than 8 ℃ within 24 hours of the lowest daily temperature; or the temperature reduction range is more than 10 ℃ within 48 hours; or the temperature is reduced by more than 12 ℃ within 72 hours, and the lowest temperature of the ground day is below 4 ℃. When one of the above conditions is satisfied, the first day satisfying the determination condition is the start day of the process, the last day satisfying the determination condition is the end day of the process, and the number of days elapsed from the start day to the end day is the number of days of the duration of the cold wave process. The maximum temperature drop range of 48 hours at the lowest daily temperature in the process Cumulative temperature reduction margin of daily minimum air temperature/>Extreme minimum air temperature of process day/>Duration of the Cold-dampness Process days/>And taking the predicted quantity as a site prediction disaster-causing factor.
And a second step of: and establishing a quantitative statistical relationship between the daily minimum air temperature and daily average air temperature data sequence of the regional station and the data sequence of the national station by using a linear fitting method by using the daily minimum air temperature and daily average air temperature data of the national station long time sequence, and expanding and reconstructing the data sequence of the regional automatic station to the same time range as the corresponding national station by using a partial least square method according to the statistical relationship. And selecting a national station with the best correlation with the data of the lowest daily air temperature and the average daily air temperature in the time period of the regional station within the range of 50 km around the regional station as a fitting object. The partial least square method can effectively find out the statistical relationship among the variables. The principle is that the factors of the matrix X are calculated by the columns of the matrix Y, and the factors of the matrix Y are predicted by the columns of the matrix X, and the mathematical model is as follows:，/> wherein the matrix elements of T and U are the fractions of X and Y, respectively And/>The matrix elements of (a) are the loading of X and Y, respectively, and E and F are the errors introduced by fitting X and Y using a partial least squares model, respectively.
The main purpose of the first two steps of the invention is to preprocess the historical air temperature data of the national station and the regional station, because the regional observation station has wider deployment in the region, the acquired meteorological data is finer, but the regional station is often deployed after 2000 (especially after 2008), the data before 2000 is lacking, the earlier meteorological data is lacking when the risk of the cold and tide disasters is pre-estimated, at the moment, the historical data of the national observation station is needed to be used for carrying out ideal relation fitting, the regional observation long-time sequence meteorological data is expanded and reconstructed, and a data basis is provided for the subsequent model construction.
And a third step of: based on intelligent grid air temperature forecast, a disaster-causing risk pre-evaluation model of the cold weather process is built, and the disaster-causing risk of the cold weather process is pre-evaluated.
1. Normalization treatment of meteorological disaster-causing factors in each cold tide disaster process:
（1）
In the formula (1), the components are as follows, Data representing weather disaster factors of cold and damp disasters of the ith observation station,/>Representing the minimum value of weather disaster causing factor data of cold tide disasters of observation station,/>Representing the maximum value of the weather disaster causing factor data of the cold tide disasters of the observation station;
2. And (3) determining the weight of each meteorological disaster-causing factor: the weight determination process of the information entropy weight method is as follows: the evaluation system is a system formed by n chill processes of m disaster causing factors, firstly, calculating the index value of the jth chill process under the ith disaster causing factor The specific gravity of the occupied index/>:
（2）
Then, the entropy value of the ith disaster causing factor is calculated by an information entropy weight methodAs shown in the formula (3),
（3）
Then, the objective weight of the ith disaster causing factor is obtained through the calculation of the formula (4)，
（4），
3. Constructing a cold tide weather process risk pre-evaluation model:
In the formula (5), the amino acid sequence of the compound, Represents the lowest temperature drop range of 48 hours/dayRepresenting the cumulative temperature drop amplitude of the lowest daily air temperature,/>Representing extreme minimum air temperature,/>Indicating duration days of the cold tide process, wherein A, B, C and D represent corresponding weight coefficients
Fourth step: comprehensively considering the pre-evaluation risk indexes of the cold weather process, and combining disaster-bearing bodies such as population, economy (GDP) and wheat to establish a cold weather process risk pre-evaluation model of the three disaster-bearing bodies.
（6）
In the formula (6), the amino acid sequence of the compound,Representing risk prediction index of k-type disaster-bearing body,/>The risk prediction index of the weather process of the cold and damp of the k-th disaster-tolerant body is represented; /(I)The exposure degree of the k-type disaster-bearing body is represented by the density of people and mouth in the area, the GDP of the ground and the proportion of the wheat planting area, the representation relational expression is shown as the following formula (7),
（7）
In the formula (7), S represents the total area of the area or the cultivated land area,The number of disaster-bearing bodies or the planting area in the area are represented,
The vulnerability index representing the k-type disaster-bearing body is obtained through the calculation of the formula (8),
（8）
In the formula (8), the amino acid sequence of the compound,Represents the area of direct economic loss or disaster in the population under 14 years old and over 65 years old, and S is the total population, the total domestic production value or the total crop planting area. And carrying out normalization processing on each evaluation index to obtain vulnerability indexes of different disaster-bearing bodies.
Fifth step: based on a disaster risk and risk pre-evaluation model of the weather process of the chill, generating a product for estimating the disaster risk and risk of various weather processes of the chill in time intervals, industries and areas.
As shown in Table 1, the risk prediction grade distribution of the weather process of Jiangsu province in the weather of 28 th 11 th to 12 th 3 rd of 2023 years obtained by applying the method of the invention is shown.
TABLE 1 risk pre-evaluation grade distribution for cold and tide processes from 28 days 11, month 28, and 3 days 12, in 2023, jiangsu province
Tables 2-4 show the risk prediction grade distribution of three disaster-bearing bodies, namely population, GDP and wheat, in the cold tide process by applying the method of the invention.
Table 2 population risk pre-evaluation grade distribution in the process of cold and tide in 2023, 11, 28, 12 and 3 days of Jiangsu province
Table 3 economic risk pre-evaluation grade distribution in cold and tide process from 11, 28, 11, 12 and 3 days of Jiangsu province 2023
Table 4 wheat Risk Pre-evaluation grade distribution in the cold and tide process from 11, 28, 12 and 3 days of 2023, jiangsu province
The method for pre-evaluating the risk of the cold tide disaster based on intelligent grid air temperature prediction extracts the disaster causing factor of the cold tide weather process prediction, combines population, economy (GDP) and wheat disaster bearing body data, constructs a model for pre-evaluating the risk and risk of the cold tide process, and develops a product for pre-evaluating the risk and a product for early warning the risk of the cold tide weather process by utilizing intelligent grid air temperature prediction products. The product can effectively make up for the limitation of weather service only relying on weather element forecast, enriches weather service information such as disaster possibility, hazard degree and the like in the process of disaster-induced weather of the chill and the tide in time intervals, industries and areas, and provides scientific decision support for effectively treating the chill and the tide, deploying disaster defending measures in advance, reducing disaster loss and the like.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (1)
1. A cold tide disaster risk pre-evaluation method based on intelligent grid air temperature prediction is characterized by comprising the following steps:
Step S1, selecting and obtaining weather disaster factors of the cold tide disasters by using disaster damage curves according to cold tide disasters and disaster history data of national observation stations and regional observation stations, wherein the weather disaster factors of the cold tide disasters comprise the lowest temperature cooling range of 48 hours Cumulative temperature reduction margin of daily minimum air temperature/>Extreme minimum air temperature/>And duration days of the Cold tide Process/>；
Step S2, screening a plurality of country observation stations within a set distance range of a certain area observation station, and fitting the country observation stations with data sequences of the same time period of the area observation station respectively by a partial least square method to obtain a statistical relationship and a correlation coefficient of the country observation stations; the data sequence is a long-time data sequence of the lowest daily air temperature and the average daily air temperature;
using the country observation station with the largest corresponding correlation coefficient as a fitting object, and expanding and reconstructing the data sequence of the regional observation station to the same time range as the corresponding country observation station by using a partial least square method through the statistical relation of the country observation station;
The expansion reconstruction of the data sequences of the other regional observation stations is completed according to the same method;
step S3, constructing a disaster risk assessment model of the cold weather process based on intelligent grid air temperature forecast to obtain a risk pre-assessment index of the cold weather process of each site;
s4, comprehensively considering a pre-evaluation risk index of the cold weather process, and combining a disaster-bearing body to establish a cold weather process risk pre-evaluation model of the disaster-bearing body, wherein the disaster-bearing body comprises population, economy and wheat;
step S5, based on a disaster risk assessment model of the cold weather process and a risk pre-assessment model of the cold weather process, generating a plurality of cold weather process disaster risk and risk estimated products of different periods, different industries and different areas;
the calculating of the risk pre-evaluation index in the cold weather process in the step S3 comprises the following steps:
(1) Normalizing the cold and damp disaster weather disaster factors of each observation station according to the following formula (1) to obtain normalized tidal disaster weather disaster factors ，
（1）
In the formula (1), the components are as follows,Represents the/>Cold and tide disaster weather disaster causing factor data of individual observation stations,/>Representing the minimum value of weather disaster causing factor data of cold tide disasters of observation station,/>Representing the maximum value of the weather disaster causing factor data of the cold tide disasters of the observation station;
(2) Weight determination of the weather disaster-causing factors of the cold and the tide:
First, the first is calculated by the formula (2) />, Under personal disaster-causing factorsIndex value/>, of secondary cold weather processSpecific gravity of the index，
（2）
In the formula (2), m represents the total number of disaster causing factors; n represents the number of times of the weather process of the chill;
Then, the information entropy weight method is calculated to obtain the first Entropy value of individual disaster causing factor/>As shown in the formula (3),
（3）
Then, the first calculation is performed by the formula (4)Objective weight of individual disaster causing factors/>，
（4），
(3) Constructing a risk prediction evaluation model of the cold weather process through the method (5) to obtain a risk prediction index of the cold weather process of each site，
（5）
In the formula (5), the amino acid sequence of the compound,Represents the lowest temperature drop range of 48 hours/dayRepresenting the cumulative temperature drop amplitude of the lowest daily air temperature,/>Representing extreme minimum air temperature,/>The duration days of the cold tide process are represented, and A, B, C and D represent corresponding weight coefficients;
Step S4, establishing a risk pre-evaluation model of the cold and damp weather process of the disaster-bearing body through a step (6),
（6）
In the formula (6), the amino acid sequence of the compound,Representing risk prediction index of k-type disaster-bearing body,/>The risk prediction index of the weather process of the cold and damp of the k-th disaster-tolerant body is represented;
The exposure degree of the k-type disaster-bearing body is represented by the density of people and mouth in the area, the GDP of the ground and the proportion of the wheat planting area, the representation relational expression is shown as the following formula (7),
（7）
In the formula (7), the amino acid sequence of the compound,Representing the total area or cultivated area of the area,/>The number of disaster-bearing bodies or the planting area in the area are represented,
The vulnerability index representing the k-type disaster-bearing body is obtained through the calculation of the formula (8),
（8）
In the formula (8), the amino acid sequence of the compound,Represents the area of direct economic loss or disaster in the population under the age of 14 and over the age of 65,/>For the total population, the total domestic production value or the total crop planting area.
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